SIELNet: 3-D Chaotic-Map-Based Secure Image Encryption Using Customized Residual Dense Spatial Network

• Published in: IEEE transactions on consumer electronics Vol. 69; no. 4; pp. 862 - 868
• Main Authors: Singh, Kedar Nath, Baranwal, Naman, Singh, Om Prakash, Singh, Amit Kumar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; chaotic encryption; Ciphers; Color; compression; Customization; Data integrity; Data transmission; Deep learning; Digital data; Encryption; Fifth Industrial Revolution; Image coding; Image reconstruction; imaging; Industrial development; Industry 5.0; integrity; Security
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2022.3227401

The continuous development of Industry 5.0 technology has brought great convenience to people's work and life. However, during digital data transmission and storage, the data may be accessed by unauthorized persons, resulting in privacy disclosure. Therefore, efficient data protection is always a high demand to solve this realistic problem. This work proposes a secure encryption algorithm, SIELNet, for colour images. First, we introduce a new three-dimensional chaotic map to encrypt colour images, obtaining the cipher images with a relationship to the plain images. We provide its excellent chaotic behaviour through standard randomness test. Secondly, we use a customized residual dense spatial network to perform the task of lossy image reconstruction from an encrypted, compressed image, which solves the constrained super-resolution task. Extensive experimental results on four public datasets demonstrate the superior performance of SIELNet against state-of-the-art techniques with excellent reconstruction quality. We believe the secure design of SIELNet can contribute to the favourable data integrity application of Industry 5.0.